James Wang
Software Engineer at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJames Wang is a software engineer with 11 years of experience focused on ML infrastructure and cloud-native systems, currently building at Google and previously contributing to core open-source projects like PyTorch and Ray. He has helped extend Ray's cluster launcher and autoscaler to treat Google Cloud TPUs as first-class resources and has deep experience optimizing TensorFlow models and TPU training pipelines. His background spans production-grade systems work—from a C++ RPC server for AWS Trainium testing to full-stack web apps and robotics research—reflecting strong versatility across languages and domains. A top student from UT Austin (3.99 GPA) who ranks highly academically, he pairs rigorous theoretical grounding with hands-on contributions that make large-scale ML compute more accessible and efficient.
11 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Mathematics and Computer Science, 3.99, Bachelor's degree, Mathematics and Computer Science, 3.99 at The University of Texas at AustinIB Diploma Recipient, IB Diploma Recipient at International BaccalaureateHigh School Diploma, Rank 5 out of 652, High School Diploma, Rank 5 out of 652 at Westwood High School
Github Skills (23)
autoscaling10
google-cloud-platform10
python10
image-classification10
machine-learning10
cloud-infrastructure10
tpu10
gcp10
tensorflow10
ray10
deploying9
distributed-systems9
model-optimization9
devops9
transformer-models9
Programming languages (4)
Github contributions (5)
tensorflow/tpu
Apr 2019 - Dec 2022
Reference models and tools for Cloud TPUs.
Role in this project:
ML Engineer
ML EngineerContributions:244 commits, 378 PRs, 578 pushes in 3 years 8 months
Contributions summary:James contributed to the `tensorflow/tpu` repository by merging internal changes into the public repository. These changes involved modifications to training and evaluation pipelines, including the integration of training cycles and evaluation steps. The user also fixed a Python3 bug in `retinanet_model.py`. Additionally, the user added new metrics into the model such as width and height and added features for colab.
cloud
tensorflow/models
Oct 2019 - Apr 2021
Models and examples built with TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:1 review, 124 commits, 1 PR in 1 year 5 months
Contributions summary:James primarily focused on updating and modifying code related to the `tensorflow/models` repository, which suggests a specialization in TensorFlow-based models. Their work included updating data download scripts for transformer models, internal changes to hyperparameter flags and the image classification pipeline, and porting and adding MLIR benchmarks for ResNet and RetinaNet models. The user's commits demonstrate an understanding of model optimization and the intricacies of the TensorFlow ecosystem.
deep-learningtensorflow
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